Airborne data files

in metadata:

The time of the pixel acquition is saved in the meta radiance info. The radiance metadata data frame is all of the flightlines for that year, filtered to preferred location. May include NA areas. Match to reflectance data using pixel location (cellrow, cellcol)

spectra data: * one column for values at each location * wl and band ID * reflectance values are scaled by 1000

Take the interpolated spectra and estimate above water remote sensing reflectance (with and without effects of solar zenith angle) as well as below water remote sensing reflectence. The remote sensing reflectance spectra should be more copmarable? but maybe would still need to be normalized. Effects of glint are not accounted for.

mysite = 'PRLA'
# my_aop_yr = '2019'
# my_loc = pref_locs %>% dplyr::filter(siteID %in% mysite) %>% pull(namedLocation)
my_loc <- c(glue('{mysite}.AOS.buoy.c0'), glue('{mysite}.AOS.buoy.c1'))

my_spectra_fileIDs <- str_detect(spectra_files, glue::glue('{mysite}'))
# my_spectra_fileIDs <- str_detect(spectra_files, glue::glue('{mysite}.*({my_aop_yr})'))
flightlines <- basename(spectra_files[my_spectra_fileIDs]) %>%
  purrr::map(~str_split(.x, pattern = "_", simplify = TRUE)[6:7]) %>%
  purrr::map_chr(~glue('{.x[[1]]}_{.x[[2]]}'))
flightlines
##  [1] "20160629_154247" "20160629_154902" "20160629_155516" "20170621_205644"
##  [5] "20190726_151709" "20190726_162146" "20190726_163004" "20190727_152346"
##  [9] "20190730_153032" "20200624_161319" "20200624_162051" "20200624_162804"
## [13] "20200624_173036" "20200626_192243" "20200702_171105"

For each flightline, check if there is data then plot

# pick a flightline to work with!
# my_flightline <- flightlines[2]
# need: file vectors created above for a site year (to get flightlines in first place)
source('R/process-flightline-refl.R')
fl_plots <- purrr::map(flightlines, ~process_flightline_refl(.x))

Looks like poor quality data for yellow conditions

fl_data <- fl_plots %>% map(~.x[['data']])
names(fl_data) <- flightlines
fl_data_df <- bind_rows(fl_data, .id = 'flightline_id')
fl_data_long <- fl_data_df %>% tidyr::pivot_longer(cols = c(rho_approx, Rrs1, Rrs2, rrs1, rrs2))
fl_data_long_green <- fl_data_long %>% dplyr::filter(my_clouds %in% c('Green (<10%) cloud cover'))

flightline_id my_theta my_gpstime_hrs
20190726_151709 49.71099 15.33982
20190726_162146 39.87219 16.36991
20190727_152346 48.78746 15.45125
20200624_161319 36.73749 16.31230
20200624_162051 35.64215 16.43769
20200624_173036 26.83384 17.60777
20200702_171105 29.60470 17.23627

More plots with full reflectance spectra etc. below

# process_flightline_refl(flightlines[5])

fl_plots[[1]]$plot1

fl_plots[[1]]$plot2

fl_plots[[2]]$plot1

fl_plots[[2]]$plot2

fl_plots[[4]]$plot1

fl_plots[[4]]$plot2

fl_plots[[5]]$plot1

fl_plots[[5]]$plot2

fl_plots[[6]]$plot1

fl_plots[[6]]$plot2

fl_plots[[8]]$plot1

fl_plots[[8]]$plot2

fl_plots[[9]]$plot1

fl_plots[[9]]$plot2

fl_plots[[10]]$plot1

fl_plots[[10]]$plot2

fl_plots[[11]]$plot1

fl_plots[[11]]$plot2

fl_plots[[13]]$plot1

fl_plots[[13]]$plot2

fl_plots[[14]]$plot1

fl_plots[[14]]$plot2

fl_plots[[15]]$plot1

fl_plots[[15]]$plot2